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Article Excerpt
Reducing disparities in health and health care by race/ethnicity and socioeconomic status (SES) is a significant health policy goal; accurate measurement of these disparities is a critical first step (Institute of Medicine 2002; National Research Council 2004). Without accurate measurement, policy makers will not be able to target the patients or providers in greatest need of intervention nor to assess the effectiveness of such interventions.
Consumer evaluations of health care, such as those from the Consumer Assessments of Healthcare Providers and Systems (CAHPS[R]) project sponsored by the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare and Medicaid Services (CMS), are a vital source of information for understanding disparities in health and health care by race/ ethnicity, SES, disability or other characteristics defining vulnerable subgroups (Weech-Maldonado et al. 2001, 2003, 2004, 2008b; Bernard, Brody, and West 2004b; Onstad 2005). Dimensions such as the courtesy and respect with which patients are treated and the clarity of communication are best assessed through consumer reports about their care. Disparities in the CAHPS composites (but not the global ratings) are targeted by Healthy People 2010 (Office of Disease Prevention and Health Promotion 2008), both as a means of reducing disparities in health outcomes and because there is an inherent interest in guaranteeing patient access to health care that is respectful and allows them to participate in treatment and care decisions. The World Health Organization (WHO) notes that there are "nonfinancial dimensions of quality of care that are important because they reflect respect for human dignity" (de Silva and Valentine 2000), a sentiment echoed by U.S. policy makers (Frist 2005).
Unfortunately, the contribution of patient experience surveys to understanding health disparities has been limited by measurement difficulties that arise from systematic differences in how patients respond to surveys. In particular, the 0-10 CAHPS global ratings, which have potential as summative measures for evaluating national and health-plan initiatives (e.g., National Health Plan Collaborative 2006) to reduce racial and economic disparities in health and health care, have had only limited use, perhaps because of awareness of measurement limitations. In this work, we seek to reduce the extent to which differential use of response scales obscures disparities in patient experience by race/ethnicity and SES, thereby improving our ability to appropriately address those disparities. Because the elderly use health care disproportionately and are potentially vulnerable, we pursue this approach using Medicare CAHPS data.
COUNTERINTUITIVE RESULTS IN MEASURING DISPARITIES IN PATIENT EXPERIENCE
Several previous analyses of patient experience surveys have found counterintuitive patterns among subgroups, even after standard case-mix adjustment (CMA). The patterns include less positive evaluations for those with supplementary insurance (Bernard, Brody, and West 2004a) and those with higher income (Hetherington, Hopkins, and Roemer 1975), as well as more positive evaluations for African Americans (Bashshur, Metzner, and Worden 1967; Morales et al. 2001; Weech-Maldonado et al. 2001, 2003, 2004; Dayton et al. 2006) than for non-Hispanic whites.
Blacks and those with lower income do not receive care that better adheres to clinical guidelines (McGlynn et al. 2003); hence, one might not expect a priori that they would report better care experiences. The most commonly advanced explanation for these patterns has been that the experiences of disadvantaged groups result in lower expectations of care. In Medicare data, the primary focus of this work, beneficiaries have transitioned to more uniform coverage under Medicare from various levels of prior insurance coverage, where expectations about health care may have been formed (House et al. 1994). Patient expectations in turn affect satisfaction with inpatient and outpatient encounters (Jackson, Chamberlin, and Kroenke 2001; Noble et al. 2006).
RESPONSE TENDENCIES AS A POSSIBLE EXPLANATION
Paulhus (1991) defines response bias as "a systematic tendency to respond to a range of questionnaire items on some other basis than the specific item content" (p. 17). If subgroups use response scales differently, response tendencies can obscure true disparities in patient experience by race/ethnicity and SES. We consider whether differing response tendencies, beyond those typically modeled, explain the counterintuitive patterns observed.
The response tendency that has received the most attention in consumer health care evaluations is positive response tendency (PRT), a tendency for some respondents to evaluate care more positively than others, given the same underlying experiences. This form of response bias can be addressed through standard regression-based CMA (Cleary and McNeil 1988; Hall and Dornan 1990; Kane, Maciejewski, and Finch 1997; Elliott et al. 2001), which assumes that certain patient characteristics are associated with a linear shift in response tendency and seeks to offset this shift by subtracting the estimated bias from mean scores.
For example, higher educational attainment has consistently been associated with less positive evaluations of health care (Fox and Storms 1981; Elliott et al. 2001; Zaslavsky et al. 2001; O'Malley et al. 2005). A priori, one might suspect that those with better education receive better health care; hence, one might interpret a negative association between education and health care ratings as reflecting response bias. Similarly, older respondents typically evaluate care more positively than younger respondents (Elliott et al. 2001); again, this pattern probably reflects PRT more than consistently better care for older respondents.
PRT may not capture all important differences in response tendency across demographic subgroups. For example, digit preference, a tendency to systematically "round" answers to certain preferred digits (e.g., numbers ending in or 5) in the absence of instructions to do so, can bias parameter estimates from surveys (Ridout and Morgan 1991; Klesges, DeBon, and Ray 1995; Crawford, Johannes, and Stellato 2002). Here we will focus on another form of response bias, extreme response tendency (ERT), which may be especially important when comparing consumer evaluations of health care.
ERT
ERT (Hamilton 1968) is a systematic tendency for some respondents to prefer the endpoints of a response scale more than other respondents, given the same underlying experiences. A subgroup with higher ERT will have higher probabilities of endorsing both highly positive and highly negative values as opposed to intermediate values, relative to a reference group with lower ERT. ERT has been demonstrated to be stable for a given respondent across a broad variety of attitude items and over time in a large panel survey from the marketing literature (Greenleaf 1992b). Greerdeaf (1992a) decomposes survey-reported attitudes into ERT and non-ERT components and finds that ERT components fail to predict corresponding reported behaviors,...
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